# cost-optimization Published articles for cost-optimization. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## We Cut Cloud Waste Before Touching Cluster Sizes: Lessons from Running a Data Platform DevFeed: [We Cut Cloud Waste Before Touching Cluster Sizes: Lessons from Running a Data Platform]() Original publisher: [Read original article]() Author: Deepika Saini Published: 2026-09-07T06:33:31Z Content type: article Language: en Sources: [Housing.com]() Topics: [BigQuery](), [databricks](), [Optimization](), [AWS Database Migration Service](), [data-platforms](), [Platform Engineering](), [migration](), [Cloud]() Tags: [bigquery](), [cloud-computing](), [cost](), [cost-optimization](), [data-engineering](), [data-platforms](), [databricks](), [delta-lake](), [finops](), [infrastructure](), [migration](), [optimization](), [platform-engineering]() ### AI overview This article explains how a data platform team reduced cloud costs by removing obsolete BigQuery data, adjusting Delta Lake retention, right-sizing DMS infrastructure, identifying unmonitored Databricks jobs, and standardizing pipeline onboarding and cost alerts. It reports that DMS costs were cut by over 50% and that retention was reduced from 90 days to 7 days for appropriate workloads after operational validation. ### Source excerpt How orphaned BigQuery storage, Delta retention, DMS right-sizing, and Databricks System Tables became our biggest cloud cost wins. The biggest cloud cost optimization we made wasn't shrinking clusters.It was deleting data we'd forgotten we were paying for.Like most teams, our first instinct was to tune infrastructure first. Instead, we discovered a treasure trove of hidden costs: orphaned BigQuery datasets, 90-day Delta retention, 24-hour jobs no one monitored, and DMS infrastructure that no longer matched business needs.We stopped treating cloud bills as a finance problem and started treating them as a platform engineering problem.30-second takeaway Why deleting forgotten data saved more than shrinking clusters. How we cut DMS costs by over 50%. How Databricks System Tables exposed hidden 24-hour jobs. How config.metadata standardized pipeline onboarding. How weekly Slack alerts turned cost optimization into a habit. Section 1: Storage Was Our Biggest Leak -- We Were Paying to Store Data Nobody Used This is the most overlooked cost on many data platforms. Storage duplication across platforms We had already migrated several workloads from BigQuery to Databricks. Large datasets were still sitting in BigQuery long after they had stopped serving production workloads - quietly generating storage costs month after month. Nothing failed. No alerts fired. Every month, we paid for storage that no longer served production workloads.A migration isn't complete until the old storage is decommissioned.The hidden cost of long retention The next surprise came from Delta Lake retention settings. Our workspace was configured to retain deleted table data and transaction history for 90 days to support time travel. Time travel is incredibly useful. But did every table need three months of historical recovery? Not really. We reduced retention to 7 days for appropriate workloads after validating operational needs. What changed immediately: Less storage tied up in deleted data. Faster clea ## Proactive FinOps Strategies for Optimizing Cloud Savings DevFeed: [Proactive FinOps Strategies for Optimizing Cloud Savings]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [finops](), [cloud cost management](), [Optimization](), [Cloud](), [Automation]() Tags: [automation](), [cloud](), [cloud-cost-management](), [cost-optimization](), [finops](), [forecasting](), [governance](), [optimization]() ### AI overview The article argues that FinOps teams should move beyond reactive overspend reduction toward proactive savings optimization. It describes continuous visibility, automated governance, forecasting, and ongoing optimization as ways to identify missed cloud savings before inefficiency compounds. ### Source excerpt Shift your FinOps paradigm from overspending to under-saving. Discover proactive cloud cost optimization strategies. Learn more with Harness CCM. | Blog ## The AI Gateway Buyer's Guide: Beyond Routing and Tool Visibility DevFeed: [The AI Gateway Buyer's Guide: Beyond Routing and Tool Visibility]() Original publisher: [Read original article]() Author: Ritesh Patel Published: 2026-08-02T16:58:40Z Content type: opinion Language: en Sources: [Nirmata]() Topics: [Artificial Intelligence](), [Model Routing](), [ai security](), [agent observability](), [Model Context Protocol]() Tags: [ai](), [ai-gateway](), [ai-security](), [claude](), [cost-optimization](), [engineering](), [kubernetes](), [latency](), [mcp](), [routing]() ### AI overview This opinion article argues that AI gateways should not be treated as governance systems merely because they provide model routing and tool-call visibility. Routing can optimize cost and latency, while monitoring can show which tools or MCP servers were used, but governance requires deciding whether an agent action is permitted for a specific agent, with specific arguments, at a specific time. ### Source excerpt Over the past year, nearly every engineering org I talk to has reached the same milestone: AI agents are no longer a demo. They're calling real tools, against real systems, with real consequences. And nearly every one of those orgs has reached for the same... The post The AI Gateway Buyer's Guide: Beyond Routing and Tool Visibility first appeared on Nirmata. ## FinOps: Shift from Reactive Cost Cutting to Proactive Cloud Savings DevFeed: [FinOps: Shift from Reactive Cost Cutting to Proactive Cloud Savings]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-07-24T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [finops](), [Optimization](), [Cloud]() Tags: [cloud](), [cost-optimization](), [finops](), [harness](), [optimization]() ### AI overview The article argues that FinOps teams should shift from reacting to cloud-cost overruns toward proactively preventing unnecessary costs before workloads reach production. It explains that rightsizing, commitment discounts, and resource lifecycle management remain useful but are insufficient alone at scale. ### Source excerpt Shift your FinOps savings optimization strategy from reactive cuts to proactive savings. Learn how Harness helps. Explore now. | Blog ## Cloud Cost Optimization Strategy: Fix Your Approach DevFeed: [Cloud Cost Optimization Strategy: Fix Your Approach]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-07-24T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [cloud cost management](), [finops](), [Optimization]() Tags: [cloud](), [cost](), [cost-optimization](), [dashboards](), [monitoring](), [recommendations]() ### AI overview This article argues that cloud cost optimization often fails when organizations rely on outdated, reactive practices and visibility tools alone. It describes how delayed cost feedback, weak accountability, and disconnected responsibilities across engineering, finance, and platform teams can allow cloud spending to grow, and presents modern FinOps and governance frameworks as a better approach. ### Source excerpt Your cloud cost optimization strategy may be failing. Learn why traditional approaches fall short and how to build a better framework. Explore now. | Blog ## Strategic Cloud Cost Management: Evolution Guide DevFeed: [Strategic Cloud Cost Management: Evolution Guide]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-07-23T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [cloud cost management](), [finops](), [Optimization](), [Platform Engineering](), [Provisioning]() Tags: [aws](), [cloud-cost-management](), [cost-optimization](), [finops](), [governance](), [optimization](), [platform](), [platform-engineering](), [provisioning]() ### AI overview This guide explains the shift from reactive cloud cost monitoring to strategic cloud cost management. It covers the FinOps maturity path, governance frameworks, cost accountability, and optimization practices for aligning engineering and finance around cloud spending. ### Source excerpt Transform from reactive spending to strategic cloud cost management. Learn the FinOps maturity path and optimization tactics. Explore now. | Blog ## Introducing the Spend by Date Range Billing View DevFeed: [Introducing the Spend by Date Range Billing View]() Original publisher: [Read original article]() Author: Rebecca Davis Published: 2025-12-16T21:27:44Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Cloud](), [Finance](), [Optimization](), [API](), [CSV]() Tags: [api](), [billing](), [cloud](), [console](), [cost-optimization](), [digitalocean](), [download](), [reporting]() ### AI overview DigitalOcean introduces a custom date range billing view that lets customers analyze cloud spending over specific periods, with daily, weekly, and monthly breakdowns by product. The feature is available globally through the Billing Console, Cloud Console, and API, with CSV report downloads. ### Source excerpt If you've ever stared at an unexpected cloud bill spike and sifted through invoices trying to find the cause, you know how time-consuming cost investigations can be. Until now, identifying cost anomalies often meant waiting for your monthly invoice or manually calculating month-to-date usage. That changes today. All DigitalOcean customers now have access to the new custom date range billing view. This new feature is accessible in the Billing Console by going to Billing -> Insights. Additionally, you can download your report as a CSV so you can view your granular billing insights offline. What's included in this new feature This update gives you clearer, more granular visibility into your cloud costs (segmented into daily, weekly, and monthly spend by product) making budgeting and forecasting far easier. DevOps, Finance, and growing teams can now manage spend proactively instead of reacting after the fact. For all customers, this feature is available globally and fully self-service via the Cloud Console and API. Here is a close look into the features of this new billing view: Custom date range filtering: You are no longer limited to invoices for viewing your spending. Choose any start and end date to view total spend for that exact range. This makes it easy to align cloud costs with development sprints, project timelines, or product launches-and improves internal chargebacks and budgeting accuracy. Daily spend breakdown: Daily granularity is key for surfacing anomalies. Instead of a single aggregated number, you can see how your spend changes day by day. If a deployment three weeks ago caused a spike, you can pinpoint the exact day and correlate it with logs to find the root cause quickly. Cost by product breakdown: See exactly where your money is going with a breakdown of spend across services such as: Droplets Databases Spaces Bandwidth Financial adjustments at a glance: For organization-level reporting, the total costs now clearly include financial adjustments like ## Replacing AWS Step Functions with SQS FIFO queues and cutting the cost in half DevFeed: [Replacing AWS Step Functions with SQS FIFO queues and cutting the cost in half]() Original publisher: [Read original article]() Author: Alexei Liulin Published: 2025-03-14T14:47:27Z Content type: article Language: en Sources: [Homegate Engineering Blog - Medium]() Topics: [Amazon Simple Queue Service (SQS)](), [AWS Step Functions](), [Amazon Web Services]() Tags: [aws](), [aws-step-functions](), [batching](), [cloud-computing](), [cost](), [cost-optimization](), [email](), [fifo](), [lambda](), [notifications](), [push-notifications](), [queue](), [sqs](), [sqs-queue](), [workflow]() ### AI overview The article describes SMG Real Estate's Search Alerts system, which batches property-match notifications for delivery by email or mobile push notification. It explains the original AWS Step Functions implementation and its transition-cost problem, motivating a more cost-efficient approach using SQS FIFO queues. ### Source excerpt One of the core services for SMG Real Estate is Search Alerts -- a service that allows users to be notified about new properties published on ImmoScout24 and Homegate. For example, if a user is looking for a new apartment having N rooms and costing less than X, and not finding any matches now, they can create a search alert with those search criteria. When a new property matching those criteria is published, the user will be notified either via email or a mobile push notification. About the Search Alerts system Users can choose the frequency of notifications -- either every 5 minutes or every 4 hours (assuming there are any to be delivered). The vast majority of search alerts are configured with the 5-minute frequency. Just to get an idea of the scale, we have: Millions of published properties match saved search alerts every day More than a million emails and push notifications sent daily Check out our "Homegate's fast and modern search experience helps users find their dream home" blog post for an overview of how Search Alerts work. The Original Implementation In the original implementation we used an AWS Step Function to achieve the 5- minute batching of notifications: When a new property matching a search alert arrived, the StartSendNotificationProcess lambda would start a Step Function execution specific to that search alert. All it did was waiting for 5 minutes while the matches accumulated in the matches-{searchAlertId} SQS queue, which was programmatically created for that specific search alert If more matching listings arrived during the 5-minute waiting period, the StartSendNotificationProcess lambda attempted to start the step function execution with the same name. When it failed with the ExecutionAlreadyExists error, we knew there was already a SF for waiting. That way the deduplication of notification processes was guaranteed. After 5 minutes of waiting time, the Step Function execution proceeded with triggering the LoadMatches lambda that received the acc ## Optimizing Databases on Kubernetes Ep.1: Provisioning a Cluster and Configuring PostgreSQL on Kubernetes DevFeed: [Optimizing Databases on Kubernetes Ep.1: Provisioning a Cluster and Configuring PostgreSQL on Kubernetes]() Original publisher: [Read original article]() Published: 2024-12-09T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Kubernetes](), [PostgreSQL](), [Provisioning](), [CloudNativePG](), [postgresql clusters](), [Linode](), [Replication](), [hosting]() Tags: [backups](), [build-vs-buy-database-solutions](), [cloudnativepg](), [cloudnativepg-setup](), [clusters](), [cnpg-postgresql-operator](), [cost-efficient-kubernetes-clusters](), [cost-optimization](), [database-infrastructure-on-linode](), [database-optimization-on-kubernetes](), [efficient-storage-with-zfspv](), [high-availability-postgresql-kubernetes](), [kubernetes](), [kubernetes-for-databases](), [kubernetes-for-startups](), [linode](), [postgresql](), [postgresql-clusters](), [postgresql-clusters-on-kubernetes](), [postgresql-replication-on-k8s](), [production](), [production-ready-postgresql-on-k8s](), [provisioning](), [provisioning-kubernetes-clusters](), [rancher](), [rancher-local-path-persistent-volumes](), [reducing-hosting-costs-with-kubernetes](), [replication](), [scalable-databases-on-kubernetes](), [scalable-postgresql-deployments](), [self-hosted-databases-vs-managed-services](), [setting-up-postgresql-on-kubernetes]() ### AI overview This introductory video demonstrates how to provision a Kubernetes cluster on Linode and configure production-ready PostgreSQL clusters on Kubernetes using CloudNativePG, including replication and backups. It also discusses storage, cost control, and build-versus-buy trade-offs. ### Source excerpt Introduction: In this introductory episode, Jérôme Petazzoni walks through the critical steps of provisioning a Kubernetes cluster tailored for running databases like PostgreSQL. Leveraging his extensive experience, Jérôme demonstrates practical tools and techniques to establish a scalable, cost-efficient database infrastructure while laying the groundwork for future optimizations covered in this series. Provisioning Kubernetes Clusters: A step-by-step guide to creating a reliable cluster using Linode. Setting Up PostgreSQL with CNPG: Deploying production-ready PostgreSQL clusters with built-in replication and backups.